This longer post will show you some of the coding skills you’ll need for turning your existing Python code into the Python-C hybrid we call Cython. In doing so, we’ll be digging into some C static data types, to see how much faster Python code will run, and restructuring some Python code along the way for maximum speed.
With Cython, all the benefits of Python are still yours – easily readable code, fast development cycles, powerful high level commands, maintainability, a suite of web development frameworks, a huge standard library for data science, machine learning, imaging, databases and security, plus easy manipulation of files, documents and strings. You should still use Python for all these things – these are what Python does best. But you should also consider combining them with Cython to speed up the computationally intensive Python functions that needs to be fast. Continue reading “From Python To Cython”
With the impending demise of Moore’s Law, multiple cores are a common manufacturers’ workaround for improving hardware performance, whether or not your installed apps can use the parallel architecture.
And with each new release of Python, parallel programming gets even easier. But the degree to which your code can use your multiple cores will depend on the kind of problem you are trying to solve, on the implementation of Python you are running and, as it turns out, how truly parallel the underlying architecture of your system actually is.
The goal of this series of posts is to see how adaptable some of my existing code is to take advantage of multi-core hardware, to see what changes need be made to scale it, and to measure the performance improvements from the exercise. Continue reading “Parallel Python – 1: Prime Numbers”
This post describes the process I used to design an algorithm that allows you to implement a modified Sieve of Eratosthenes to bypass the memory limitations of your computer and, in the process, to find big primes well beyond your 64-bit computer’s supposed numerical limit of 2.0e63 (9.223e18). Beyond that, with this algorithm, the only limitation is the speed of your CPU.
Continue reading “Eratosthenes 2: Swifter, Further, Cooler”